Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression

arXiv:2405.01584v1 Announce Type: cross
Abstract: We propose a novel, lightweight supervised dictionary learning framework for text classification based on data compression and representation. This two-phase algorithm initially employs the Lempel-Ziv-Welch (LZW) algorithm to construct a dictionary from text datasets, focusing on the conceptual significance of dictionary elements. Subsequently, dictionaries are refined considering label data, optimizing dictionary atoms to enhance discriminative power based on mutual information and class distribution. This process generates discriminative numerical representations, facilitating the training of simple classifiers …

abstract algorithm arxiv classification compression construct cs.cl cs.lg data data compression datasets dictionary eess.sp framework information novel representation significance text text classification type